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Analyzing Prefetch Files For Execution History

  • 221 installs
  • 27.3k repo stars
  • Updated August 2, 2026
  • mukul975/anthropic-cybersecurity-skills

Analyzing Prefetch Files for Execution History is an agent skill that helps interpret Windows Prefetch artifacts to infer recent program execution during security investigations.

About

Analyzing Prefetch Files for Execution History is an agent skill aimed at solo builders and indie operators who need lightweight digital forensics without a full DFIR team. Windows Prefetch stores metadata about recently launched executables; this skill guides structured interpretation of those artifacts so you can corroborate or refute what ran on a workstation or build VM. Use it during security reviews, suspected compromise triage, or compliance checks when Event Viewer and EDR data are incomplete. The skill is phase-specific to Ship → security: it does not replace secure coding in Build, but it closes the gap when you must reason about ground truth on a Windows endpoint. Pair it with broader hardening and logging skills; treat conclusions as corroborating evidence alongside other telemetry. Because published SKILL.md in the repo is minimal, treat operational steps as agent-procedural knowledge you validate against your toolchain and jurisdiction before production IR.

  • Interprets Windows Prefetch (.pf) records to reconstruct program execution timelines
  • Supports security investigations where process logs are missing or tampered
  • Fits agent-assisted forensic workflows alongside other Anthropic cybersecurity skills
  • Apache 2.0–licensed skill package suitable for audit and IR playbooks
  • Outputs execution-history narrative suitable for post-incident reports

Analyzing Prefetch Files For Execution History by the numbers

  • 221 all-time installs (skills.sh)
  • +13 installs in the week ending Aug 4, 2026 (Skillselion tracking)
  • Ranked #735 of 2,203 Security skills by installs in the Skillselion catalog
  • Security screen: LOW risk (skills.sh audit)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill analyzing-prefetch-files-for-execution-history

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Listed on Skillselion
Installs221
repo stars27.3k
Security audit3 / 3 scanners passed
Last updatedAugust 2, 2026
Repositorymukul975/anthropic-cybersecurity-skills

What it does

Investigate which programs ran on a Windows host by interpreting Prefetch artifacts during incident response or malware triage.

Who is it for?

Best when you're doing Windows endpoint triage, malware homework, or audit support with agent assistance.

Skip if: Linux-only stacks, cloud-only forensics without Windows hosts, or teams that need certified chain-of-custody tooling instead of agent-guided analysis.

When should I use this skill?

You need to infer recent program execution on Windows using Prefetch artifacts during a security or IR review.

What you get

You produce a defensible execution-history narrative from Prefetch data that you can fold into a security report or next-step containment plan.

  • Execution-history summary from Prefetch interpretation
  • Notes for security report or follow-up containment

Files

SKILL.mdMarkdownGitHub ↗

Analyzing Prefetch Files for Execution History

When to Use

  • When determining which programs were executed on a Windows system and when
  • During malware investigations to confirm execution of suspicious binaries
  • For establishing a timeline of application usage during an incident
  • When correlating program execution with other forensic artifacts
  • To identify anti-forensic tools or unauthorized software that was run

Prerequisites

  • Access to Windows Prefetch directory (C:\Windows\Prefetch\) from forensic image
  • PECmd (Eric Zimmerman), WinPrefetchView, or python-prefetch parser
  • Understanding of Prefetch file format (versions 17, 23, 26, 30)
  • Windows system with Prefetch enabled (default on client OS, disabled on servers)
  • Knowledge of Prefetch naming conventions (APPNAME-HASH.pf)

Workflow

Step 1: Extract Prefetch Files from Forensic Image

# Mount the forensic image
mount -o ro,loop,offset=$((2048*512)) /cases/case-2024-001/images/evidence.dd /mnt/evidence

# Copy all prefetch files
mkdir -p /cases/case-2024-001/prefetch/
cp /mnt/evidence/Windows/Prefetch/*.pf /cases/case-2024-001/prefetch/

# Count and list prefetch files
ls -la /cases/case-2024-001/prefetch/ | wc -l
ls -la /cases/case-2024-001/prefetch/ | head -30

# Hash all prefetch files for integrity
sha256sum /cases/case-2024-001/prefetch/*.pf > /cases/case-2024-001/prefetch/pf_hashes.txt

# Note: Prefetch filename format is EXECUTABLE_NAME-XXXXXXXX.pf
# The hash (XXXXXXXX) is based on the executable path
# Same executable from different paths creates different prefetch files

Step 2: Parse Prefetch Files with PECmd

# Using Eric Zimmerman's PECmd (Windows or via Mono/Wine on Linux)
# Download from https://ericzimmerman.github.io/

# Parse a single prefetch file
PECmd.exe -f "C:\cases\prefetch\POWERSHELL.EXE-A]B2C3D4.pf"

# Parse all prefetch files and output to CSV
PECmd.exe -d "C:\cases\prefetch\" --csv "C:\cases\analysis\" --csvf prefetch_results.csv

# Parse with JSON output
PECmd.exe -d "C:\cases\prefetch\" --json "C:\cases\analysis\" --jsonf prefetch_results.json

# Output includes for each file:
# - Executable name and path
# - Run count
# - Last run time (up to 8 timestamps in Windows 10)
# - Files and directories referenced during execution
# - Volume information (serial number, creation date)
# - Prefetch file creation time

Step 3: Parse with Python for Linux-Based Analysis

pip install prefetch

python3 << 'PYEOF'
import os
import json
from datetime import datetime

# Parse prefetch files using python
import struct

def parse_prefetch(filepath):
    """Parse a Windows Prefetch file."""
    with open(filepath, 'rb') as f:
        data = f.read()

    # Check for MAM compressed format (Windows 10)
    if data[:4] == b'MAM\x04':
        import lznt1  # or use DecompressBuffer
        # Windows 10 prefetch files are compressed
        print(f"  [Compressed Win10 format - use PECmd for full parsing]")
        return None

    # Version 17 (XP), 23 (Vista/7), 26 (8.1), 30 (10)
    version = struct.unpack('<I', data[0:4])[0]
    signature = data[4:8]

    if signature != b'SCCA':
        print(f"  Invalid prefetch signature")
        return None

    file_size = struct.unpack('<I', data[8:12])[0]
    exec_name = data[16:76].decode('utf-16-le').strip('\x00')
    run_count = struct.unpack('<I', data[208:212])[0] if version >= 23 else struct.unpack('<I', data[144:148])[0]

    result = {
        'version': version,
        'executable': exec_name,
        'file_size': file_size,
        'run_count': run_count,
    }

    # Extract last execution timestamps
    if version == 23:  # Vista/7 - 1 timestamp
        ts = struct.unpack('<Q', data[128:136])[0]
        result['last_run'] = filetime_to_datetime(ts)
    elif version >= 26:  # Win8+ - up to 8 timestamps
        timestamps = []
        for i in range(8):
            ts = struct.unpack('<Q', data[128+i*8:136+i*8])[0]
            if ts > 0:
                timestamps.append(filetime_to_datetime(ts))
        result['last_run_times'] = timestamps

    return result

def filetime_to_datetime(ft):
    """Convert Windows FILETIME to datetime string."""
    if ft == 0:
        return None
    timestamp = (ft - 116444736000000000) / 10000000
    try:
        return datetime.utcfromtimestamp(timestamp).strftime('%Y-%m-%d %H:%M:%S UTC')
    except (OSError, ValueError):
        return None

# Process all prefetch files
prefetch_dir = '/cases/case-2024-001/prefetch/'
results = []

for filename in sorted(os.listdir(prefetch_dir)):
    if filename.lower().endswith('.pf'):
        filepath = os.path.join(prefetch_dir, filename)
        print(f"\n=== {filename} ===")
        result = parse_prefetch(filepath)
        if result:
            print(f"  Executable: {result['executable']}")
            print(f"  Run Count:  {result['run_count']}")
            if 'last_run' in result:
                print(f"  Last Run:   {result['last_run']}")
            elif 'last_run_times' in result:
                for i, ts in enumerate(result['last_run_times']):
                    print(f"  Run Time {i+1}: {ts}")
            results.append(result)

# Save results
with open('/cases/case-2024-001/analysis/prefetch_analysis.json', 'w') as f:
    json.dump(results, f, indent=2)
PYEOF

Step 4: Identify Suspicious Execution Evidence

# Search for known malicious tool names in prefetch
ls /cases/case-2024-001/prefetch/ | grep -iE \
   '(MIMIKATZ|PSEXEC|WMIC|COBALT|BEACON|PWDUMP|PROCDUMP|LAZAGNE|RUBEUS|BLOODHOUND|SHARPHOUND|CERTUTIL|BITSADMIN)'

# Search for script interpreters (potential malicious execution)
ls /cases/case-2024-001/prefetch/ | grep -iE \
   '(POWERSHELL|CMD\.EXE|WSCRIPT|CSCRIPT|MSHTA|REGSVR32|RUNDLL32|MSIEXEC)'

# Search for remote access tools
ls /cases/case-2024-001/prefetch/ | grep -iE \
   '(TEAMVIEWER|ANYDESK|LOGMEIN|VNC|SPLASHTOP|SCREENCONNECT|AMMYY)'

# Search for data exfiltration tools
ls /cases/case-2024-001/prefetch/ | grep -iE \
   '(RAR|7Z|ZIP|RCLONE|MEGA|DROPBOX|ONEDRIVE|GDRIVE|FTP|CURL|WGET)'

# Find recently created prefetch files (newest executables run)
ls -lt /cases/case-2024-001/prefetch/ | head -20

# Cross-reference with Shimcache and Amcache for confirmation
# Prefetch existence = program was executed at least once

Step 5: Build Execution Timeline

# Create timeline from prefetch data
python3 << 'PYEOF'
import json
import csv

with open('/cases/case-2024-001/analysis/prefetch_analysis.json') as f:
    data = json.load(f)

timeline = []
for entry in data:
    if 'last_run_times' in entry:
        for ts in entry['last_run_times']:
            if ts:
                timeline.append({
                    'timestamp': ts,
                    'executable': entry['executable'],
                    'run_count': entry['run_count'],
                    'source': 'Prefetch'
                })
    elif 'last_run' in entry and entry['last_run']:
        timeline.append({
            'timestamp': entry['last_run'],
            'executable': entry['executable'],
            'run_count': entry['run_count'],
            'source': 'Prefetch'
        })

# Sort chronologically
timeline.sort(key=lambda x: x['timestamp'])

# Write timeline CSV
with open('/cases/case-2024-001/analysis/execution_timeline.csv', 'w', newline='') as f:
    writer = csv.DictWriter(f, fieldnames=['timestamp', 'executable', 'run_count', 'source'])
    writer.writeheader()
    writer.writerows(timeline)

# Print suspicious time window
for entry in timeline:
    if '2024-01-15' in entry['timestamp'] or '2024-01-16' in entry['timestamp']:
        print(f"  {entry['timestamp']} | {entry['executable']} (x{entry['run_count']})")
PYEOF

Key Concepts

ConceptDescription
PrefetchWindows performance optimization that pre-loads application data and tracks execution
SCCA signatureMagic bytes identifying a valid Prefetch file
Path hashCRC-based hash of the executable path forming part of the .pf filename
Run countNumber of times the executable has been launched (may wrap around)
Last run timestampsWindows 8+ stores up to 8 most recent execution timestamps
Referenced filesList of files and directories accessed during the first 10 seconds of execution
Volume informationDrive serial number and creation date identifying the source volume
MAM compressionWindows 10 Prefetch files use MAM4 compression requiring decompression before parsing

Tools & Systems

ToolPurpose
PECmdEric Zimmerman's Prefetch parser with CSV/JSON output
WinPrefetchViewNirSoft GUI tool for viewing Prefetch files
python-prefetchPython library for parsing Prefetch files
Prefetch Hash CalculatorTool to calculate expected hash from executable paths
KAPEAutomated artifact collection including Prefetch
AutopsyForensic platform with Prefetch analysis module
Plaso/log2timelineSuper-timeline tool that includes Prefetch parser
VelociraptorEndpoint agent with Prefetch collection and analysis artifacts

Common Scenarios

Scenario 1: Confirming Malware Execution Search Prefetch directory for the malware executable name, confirm execution via Prefetch existence, extract run count and last run time, identify referenced DLLs to understand malware behavior, correlate with registry autorun entries.

Scenario 2: Attacker Tool Usage Timeline Identify Prefetch files for PsExec, Mimikatz, BloodHound, and other attacker tools, build chronological timeline of tool execution, determine the sequence of the attack (reconnaissance, credential theft, lateral movement), match timestamps with network connection logs.

Scenario 3: Data Staging and Exfiltration Look for Prefetch entries of compression tools (7z, WinRAR, zip), identify execution of file transfer utilities (rclone, FTP clients), check for cloud storage client execution, timeline when data staging and transfer occurred.

Scenario 4: Anti-Forensics Detection Check for execution of known anti-forensic tools (CCleaner, Eraser, SDelete), identify if Prefetch directory was recently cleared (fewer files than expected for active system), note timestamps of anti-forensic tool execution relative to other evidence.

Output Format

Prefetch Analysis Summary:
  System: Windows 10 Pro (Build 19041)
  Prefetch Files: 234
  Analysis Period: All available execution history

  Execution Statistics:
    Total unique executables: 234
    First execution: 2023-06-15 (system install)
    Latest execution: 2024-01-18 23:45 UTC

  Suspicious Executions:
    MIMIKATZ.EXE-5F2A3B1C.pf
      Run Count: 3 | Last: 2024-01-16 02:30:15 UTC
    PSEXEC.EXE-AD70946C.pf
      Run Count: 7 | Last: 2024-01-16 02:45:30 UTC
    RCLONE.EXE-1F3E5A2B.pf
      Run Count: 2 | Last: 2024-01-17 03:15:00 UTC
    POWERSHELL.EXE-022A1004.pf
      Run Count: 145 | Last: 2024-01-18 14:00:00 UTC

  Attack Timeline (from Prefetch):
    2024-01-15 14:32 - POWERSHELL.EXE (initial access)
    2024-01-16 02:30 - MIMIKATZ.EXE (credential theft)
    2024-01-16 02:45 - PSEXEC.EXE (lateral movement)
    2024-01-17 03:15 - RCLONE.EXE (data exfiltration)

  Report: /cases/case-2024-001/analysis/execution_timeline.csv

Related skills

How it compares

Use for Windows artifact forensics rather than generic “scan my repo for secrets” security skills.

FAQ

Who is analyzing-prefetch-files-for-execution-history for?

Developers, security-curious founders, and small teams investigating Windows endpoints who want structured Prefetch interpretation via Claude Code, Cursor, or similar agents.

When should I use analyzing-prefetch-files-for-execution-history?

During Ship-phase security work: post-incident triage on a dev laptop, validating what ran on a build VM, or supplementing logs before you ship updates from a compromised-suspect machine.

Is analyzing-prefetch-files-for-execution-history safe to install?

Review the Security Audits panel on this Prism page and the upstream repo license; forensics skills may imply handling sensitive host data—run only on systems you own or are authorized to examine.

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